


'''
Config params
'''
import os

# main_dir = os.path.join(os.getcwd(),'data_char')
# name_list_path = os.path.join(os.getcwd(),'data_char/list.txt')
# sketch_dir = os.path.join(os.getcwd(),'data_char/sketch')
# dnfs_dir = os.path.join(os.getcwd(),'data_char/dnfs')

home=os.path.expanduser('~')
main_dir=os.path.join(home,'TrainingData','Character')
sketch_dir=os.path.join(main_dir,'sketch')
dnfs_dir=os.path.join(main_dir,'dn')
name_list_path=os.path.join(main_dir,'train-list.txt')
#name_list_path=os.path.join(main_dir,'temp_list.txt')

# #is training
# is_training=True
# #sketch val
# sketch_value=1
# # Logging
# log_dir=os.path.join(home,'holojest','Sketch','logs')
# train_log_dir=os.path.join(log_dir,'train')
# if not os.path.exists(train_log_dir):
#     os.mkdir(train_log_dir)
#
# eval_log_dir=os.path.join(log_dir,'eval')
# if not os.path.exists(eval_log_dir):
#     os.mkdir(eval_log_dir)
#
# checkpoints_dir=os.path.join(home,'holojest','Sketch','checkpoints')
#
# is_adversial= True
# Loss tuning
loss_normalize=True
mask_threshold=0.9
lambda_pixel=1
lambda_adv=0.01
# Train configs
training_iter = 100
batch_size = 2
learning_rate = .0001

# Data
prefetch_buffer_size = 2
num_parallel_batches = 2
